Handwritten Form Recognition via User Feedback and Image Preprocessing
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Solution Overview
Problem
Conventional methods for processing handwritten data on paper forms are prone to errors, time-consuming, and costly, especially when using manual entry or low-quality camera images for Optical Character Recognition (OCR), which are not accurate due to variations in handwriting and image quality issues.
Innovation Solution
A system that captures images of handwritten forms using a camera or scanner, processes them for quality enhancement, and allows user feedback to correct recognition errors, providing visual and textual feedback to improve future writing and training a machine learning model for improved recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data entry operators are used to process handwritten forms, then data can be entered into computer systems, but errors occur due to lack of attention or inability to comprehend handwriting accurately
Solution Approach 1:
The system implements a feedback loop where users can correct recognition errors, and these corrections are used to retrain the machine learning model. This continuous feedback mechanism improves recognition accuracy over time while reducing the need for manual intervention in future processing.
Solution Approach 2:
The system enables self-service by allowing users to easily correct recognition errors through a simple interface. The corrected data is automatically processed and used for model improvement, eliminating the need for extensive manual data entry and verification.
2Productivity
If OCR tools are used to recognize handwritten text, then manual data entry is eliminated and processing speed improves, but accuracy falls significantly due to variations in handwriting
Solution Approach 1:
The system performs preliminary actions by pre-processing images to enhance quality before recognition. It also pre-trains the machine learning model with diverse handwriting samples to improve its ability to handle variations in handwriting styles before actual recognition occurs.
Solution Approach 2:
The system changes parameters by adjusting the machine learning model based on feedback from user corrections. The model's internal parameters are updated through retraining, allowing it to adapt to specific handwriting patterns and improve accuracy for particular users or contexts.
3Ease of operation
If camera-captured images are used as input for OCR, then portability and ease of data collection improve, but image quality suffers due to lighting, tilt, blur, and low resolution
Solution Approach 1:
The system performs preliminary image processing to correct quality issues before recognition. It applies corrections for lighting variations, removes blur, adjusts for tilt and skew, and enhances resolution to compensate for the limitations of camera-captured images.
Solution Approach 2:
The system introduces an intermediary image processing stage between image capture and OCR recognition. This intermediary layer processes and enhances the image quality, acting as a mediator that bridges the gap between low-quality camera inputs and the requirements of accurate character recognition.
Data Source
AI summary
System and method for improving recognition of characters. A system for improving recognition of characters is disclosed. The system comprises at least one processor (10), configured to receive an image (1004) of an article (102) comprising characters to be recognized. The system (100) displays characters as recognized on a display screen (1006). Further, the system (100) is configured to receive user feedback comprising correction of an error made by the system (100) in recognizing at least one character and provide a system feedback comprising display of images or textual descriptions of one or more variants (1012, 1014, 1016, 1018, 1020, 1022) of a character, which is incorrectly recognized by the system, which enables the natural person to adapt writing style to enable better quality inputs to the recognition module. The article (102) is a handwritten paper form (102), filled and captured by the natural person.